However, few studies have focused on the dimensionality selection problem. Most research evaluating the effectiveness of word embedding focuses on word analogy and word similarity tasks [1]}. These evaluation methods require handcrafted datasets for implementation, but such datasets are currently not available to evaluate model training on non-English verbal and non-verbal data. To the best of our knowledge, only Yin and Shen [2]} accomplished the dimensionality selection of a word embedding model without the use of evaluation datasets. However, two aspects about this method need further consideration: the assumption that the noise signal obeys the zero mean-Gaussian distribution has not been verified in real data, and the selected dimensionality is quite different from those obtained by the other evaluation methods based on handcrafted datasets.
